CN114940132B - Electric vehicle endurance mileage prediction method, test method and system - Google Patents

Electric vehicle endurance mileage prediction method, test method and system Download PDF

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CN114940132B
CN114940132B CN202210887943.5A CN202210887943A CN114940132B CN 114940132 B CN114940132 B CN 114940132B CN 202210887943 A CN202210887943 A CN 202210887943A CN 114940132 B CN114940132 B CN 114940132B
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test
electric vehicle
motor
endurance mileage
determining
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CN114940132A (en
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王伟
曲辅凡
孙龙
张晓辉
李文博
雷利刚
李飞
郭瑞玲
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CATARC Automotive Test Center Tianjin Co Ltd
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CATARC Automotive Test Center Tianjin Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
    • B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/26—Vehicle weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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Abstract

The invention relates to the field of automobiles, and discloses a method, a method and a system for predicting the endurance mileage of an electric vehicle. The prediction method comprises the following steps: determining a first characteristic factor of which the relevance with the endurance mileage is greater than a first set threshold in data obtained based on an electric vehicle endurance mileage testing method; determining a second characteristic factor of which the relevance with the test working condition is greater than a second set threshold value from the first characteristic factor; training the neural network model by taking the second characteristic factors as training samples to obtain a neural network prediction model; and inputting relevant factors of the test working condition to be predicted into the neural network prediction model to obtain the endurance mileage and relevant control factors. The embodiment realizes accurate prediction of the driving mileage of the electric vehicle under different temperature environments and different working conditions.

Description

Electric vehicle endurance mileage prediction method, test method and system
Technical Field
The invention relates to the technical field of automobiles, in particular to a method, a method and a system for predicting the driving mileage of an electric vehicle.
Background
The endurance mileage is an important performance of the electric automobile, and a plurality of factors influencing the endurance mileage of the electric automobile are provided, wherein the endurance mileage of the electric automobile is greatly influenced by high-temperature and low-temperature environments. And the high and low temperature environment has larger influence on the charge and discharge efficiency of the battery, the motor efficiency of the automobile and the energy consumption of the air conditioner.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a method and a system for predicting the endurance mileage of an electric vehicle, which realize accurate prediction of the endurance mileage of the electric vehicle under different temperature environments and different working conditions.
The embodiment of the invention provides a method for predicting the driving mileage of an electric vehicle, which is executed based on data obtained by a method for testing the driving mileage of the electric vehicle, and comprises the following steps:
determining a first characteristic factor of which the relevance with the endurance mileage is greater than a first set threshold in the data obtained based on the electric vehicle endurance mileage testing method;
determining a second characteristic factor with the relevance to the test working condition larger than a second set threshold value from the first characteristic factor;
training a neural network model by taking the second characteristic factors as training samples to obtain a neural network prediction model;
inputting relevant factors of the testing working condition to be predicted into the neural network prediction model to obtain the endurance mileage and relevant control factors;
the relevant factors of the test working condition to be predicted comprise one or more of ambient temperature, battery capacity, battery efficiency, motor efficiency, air conditioner power, vehicle quality and vehicle running resistance; the related control factors comprise one or more of battery energy loss, motor energy loss and air conditioner energy consumption;
the data obtained based on the electric vehicle endurance mileage testing method comprises the following factors and the corresponding relation among any several factors: the system comprises an environmental chamber temperature, a battery efficiency, a battery capacity, a motor efficiency, air conditioner energy consumption, a test working condition, energy consumption required by the test working condition, vehicle quality and endurance mileage.
The embodiment of the invention provides a method for testing the driving mileage of an electric vehicle, which is applied to a system for testing the driving mileage of the electric vehicle, and comprises the following steps:
according to preset test parameters, a bearing frame in the electric vehicle endurance mileage test system is used for setting matched running resistance coefficients and test working conditions, and an air conditioning system of the electric vehicle and the temperature of an environment cabin for simulating the environment temperature are correspondingly set;
when the set conditions are met, starting the test system to carry out the endurance mileage test of the electric vehicle;
in the process of testing the endurance mileage of the electric vehicle, an upper computer in the electric vehicle endurance mileage testing system acquires test data through a data acquisition unit;
when an error between a real-time speed curve displayed by a display screen in the electric vehicle endurance mileage testing system and the curve of the testing working condition reaches an error threshold value, stopping the electric vehicle endurance mileage test, and when the electric vehicle endurance mileage test is stopped, determining the electric quantity consumption of a power battery of the electric vehicle in the test based on the acquired testing data through the upper computer and recording the driving mileage of the test through the bearing frame;
the preset test parameters comprise one or more of environment bin temperature, test working conditions, the quality of the electric vehicle and relevant parameters of an air conditioner of the electric vehicle.
The embodiment of the invention provides a system for testing the endurance mileage of an electric vehicle, which comprises:
the device comprises an upper computer, a display screen, a bearing frame, three current sensors, three voltage sensors, two temperature sensors, an environment bin and a data acquisition unit;
the upper computer is in communication connection with the data acquisition unit and is used for receiving and storing the data acquired by the data acquisition unit;
the bearing frame is used for performing mileage test on the electric vehicle;
the three current sensors are respectively used for detecting the current of a power battery of the electric vehicle, the current of an air conditioning system of the electric vehicle and the current of a motor of the electric vehicle when the driving range of the electric vehicle is tested;
the three voltage sensors are respectively used for detecting the voltage of a power battery of the electric vehicle, the voltage of an air conditioning system of the electric vehicle and the voltage of a motor of the electric vehicle when the driving mileage test is carried out on the electric vehicle;
the two temperature sensors are respectively used for detecting the temperature inside the electric vehicle and the temperature of the environment cabin;
the data acquisition unit is used for acquiring current data of the three current sensors, voltage data of the three voltage sensors, temperature data of the two temperature sensors and data of a CAN bus of the electric vehicle;
the display screen is in communication connection with the bearing frame and used for displaying a test working condition and a real-time speed fed back by the bearing frame so as to determine the finish time of the endurance mileage test of the electric vehicle based on the test working condition and the real-time speed.
An embodiment of the present invention provides an electronic device, including:
a processor and a memory;
the processor is used for executing the steps of the electric vehicle endurance mileage testing method according to any embodiment by calling the program or the instructions stored in the memory.
The embodiment of the invention provides a computer-readable storage medium, which stores a program or instructions, wherein the program or instructions enable a computer to execute the steps of the electric vehicle endurance mileage testing method according to any one of the embodiments.
The embodiment of the invention has the following technical effects:
the driving mileage of the electric vehicle under different temperature environments and different working conditions can be accurately predicted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic structural diagram of a driving range testing system of an electric vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for testing the driving mileage of an electric vehicle according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a driving range testing method according to an embodiment of the present invention;
FIG. 4 is a method for predicting the driving mileage of an electric vehicle according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a method for testing the driving mileage of an electric vehicle according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the disclosed embodiments are merely exemplary of the invention, and are not intended to be exhaustive or exhaustive. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The driving mileage testing system of the electric vehicle provided by the embodiment of the invention is mainly suitable for testing the driving mileage of the electric vehicle under different temperature environments and different working conditions.
Fig. 1 is a schematic structural diagram of a system for testing a driving range of an electric vehicle according to an embodiment of the present invention. Referring to fig. 1, the electric vehicle endurance mileage testing system specifically includes: host computer 110, display screen 120, bear frame 130, three current sensor 140, three voltage sensor 150, two temperature sensor (mark respectively as first temperature sensor 160 and second temperature sensor 170, wherein, first temperature sensor 160 is used for detecting the inside temperature of electric motor car, and second temperature sensor 170 is used for detecting the temperature in environment storehouse 180), environment storehouse 180, data acquisition unit 190 and electronic controller unit 200.
The upper computer 110 is in communication connection with the data acquisition unit 190, and is used for receiving and storing the data acquired by the data acquisition unit 190. Further, the upper computer 110 can also display some data received by the upper computer.
The data collecting unit 190 is configured to collect current data of the three current sensors 140, voltage data of the three voltage sensors 150, temperature data of the two temperature sensors 160 and 170, and data of a CAN bus of the electric vehicle. The data of the CAN bus of the electric vehicle includes one or more of a rotational speed torque of an engine of the electric vehicle, a rotational speed torque of a motor, a State of charge (SOC) of a battery, an opening degree of an accelerator pedal, an opening degree of a brake pedal, a gear, and an air conditioner power.
The bearing frame 130 is a carrier for vehicle testing and is used for mileage testing of the electric vehicle; including but not limited to a chassis dynamometer, an axle coupled dynamometer, a five motor rig, or a four motor rig. The three current sensors 140 are respectively used for detecting the current of a power battery of the electric vehicle, the current of an air conditioning system of the electric vehicle and the current of a motor of the electric vehicle when the driving range of the electric vehicle is tested. The three voltage sensors 150 are respectively used for detecting the voltage of a power battery of the electric vehicle, the voltage of an air conditioning system of the electric vehicle and the voltage of a motor of the electric vehicle when the driving range of the electric vehicle is tested. The two temperature sensors 160 and 170 are used for detecting the temperature inside the electric vehicle and the temperature of the environmental chamber 180, respectively;
the display screen 120 is in communication connection with the bearing frame 130, and is configured to display a test condition and a real-time vehicle speed fed back by the bearing frame, so as to determine an ending timing of the endurance mileage test of the electric vehicle based on the test condition and the real-time vehicle speed. The environmental chamber 180 is used to simulate the ambient temperature required for vehicle testing.
On the basis of the electric vehicle endurance mileage testing system disclosed by the embodiment, the embodiment of the invention also provides an electric vehicle endurance mileage testing method, which is applied to the electric vehicle endurance mileage testing system disclosed by the embodiment. Referring to fig. 2, a schematic flow chart of a method for testing the driving range of an electric vehicle includes the following steps:
step 210, setting matched running resistance coefficients and test conditions through the bearing frame according to preset test parameters, correspondingly setting an air conditioning system of the electric vehicle and correspondingly setting the temperature of the environment bin.
The preset test parameters comprise one or more of environment bin temperature, test working conditions, the quality of the electric vehicle and relevant parameters of an air conditioner of the electric vehicle.
The Test condition may be a standard Cycle condition, such as WLTC (World Light Vehicle Test Cycle), WLTP (World Light Vehicle Test Procedure), CLTC (China Light-duty Vehicle Test Cycle-passenger), and the like, or may be a custom condition. The relevant parameters of the electric vehicle air conditioner comprise: one or more of a cooling/heating mode setting, a temperature setting, and a wind speed setting.
The preset test parameters can be counted into a test matrix, and the test matrix of the factors such as the test temperature (namely the temperature of the environment bin), the test working condition, the vehicle quality (namely the quality of the electric vehicle), the air conditioner setting (namely the associated parameters of the air conditioner of the electric vehicle) and the like is constructed in a general way. The test temperature includes low temperature and high temperature, such as-20 deg.C, -10 deg.C, 0 deg.C, 30 deg.C, 35 deg.C, etc.; the test working conditions comprise standard working conditions, user-defined working conditions and the like, such as WLTC, CLTC, constant speed working conditions and the like; the vehicle mass includes a set mass, a half load mass, a full load mass, and the like, and the running resistance of the vehicle is affected by different vehicle masses.
For exemplary purposes, reference is made to a sample of a test matrix as shown in Table 1 below.
Table 1: test matrix
Factors of the factSerial number Test temperature (. Degree. C.) Test conditions Vehicle mass Air conditioner arrangement
1 -20 CLTC Quality of preparation Heating, 23 ℃, auto mode
2 -10 WLTC Quality of preparation Heating, 25 ℃, auto mode
3 35 WLTC Half load Refrigeration, 27 ℃, auto mode
4 40 At uniform speed Full load Refrigeration, 25 ℃, auto mode
It CAN be understood that before the test is started, a current sensor and a voltage sensor are arranged on a power battery, an air conditioning system and a motor, an output signal line is arranged on a data acquisition unit, each sensor is corrected and cleared, a CAN bus connecting line is led out from a vehicle control unit and arranged on the data acquisition unit, and the data acquisition unit is connected to an upper computer to check whether signals of each sensor and CAN signals are normally displayed and normally stored. And charging the vehicle from an empty state to a full state according to the test items designed by the test matrix, and recording the charging amount. Setting the temperature of the environment cabin as a target temperature, enabling the vehicle to be fully immersed in the environment cabin, setting a running resistance coefficient corresponding to the mass of the tested vehicle on the bearing frame, setting a test working condition, and setting the air conditioner of the vehicle according to the test matrix.
And step 220, when the set conditions are met, starting the test system to carry out the endurance mileage test of the electric vehicle.
Specifically, when the vehicle and the test system meet various test requirements indicated by the test matrix, the test system is started to carry out the endurance mileage test of the electric vehicle. In summary, referring to a schematic flow chart of a mileage test method shown in fig. 3, the method specifically includes: the method comprises the steps of test matrix construction, equipment installation and calibration, vehicle pretreatment, environment cabin and bearing frame configuration and endurance mileage test.
And step 230, in the process of testing the endurance mileage of the electric vehicle, the upper computer acquires test data through the data acquisition unit.
And 240, when the error between the real-time speed curve displayed by the display screen and the curve of the test working condition reaches an error threshold value, stopping the endurance mileage test of the electric vehicle, and when the endurance mileage test of the electric vehicle is stopped, determining the electric quantity consumption of the power battery of the electric vehicle in the test based on the collected test data through the upper computer and recording the driving mileage of the test through the bearing frame.
After the test is finished, the upper computer automatically records the test data uploaded by the data acquisition unit, the electric quantity consumption of the test is automatically calculated, and the bearing frame automatically records the driving mileage of the test.
Further, the test data collected by the data collection unit at least includes: the voltage and the current of a power battery of the electric vehicle, the voltage and the current of an air conditioning system of the electric vehicle and the voltage and the current of a motor of the electric vehicle;
the method further comprises the following steps:
determining the accumulated discharge capacity of the power battery according to the test data acquired by the data acquisition unit; determining the battery efficiency of the power battery according to the accumulated discharge amount and the charge amount; determining the power consumption of the air conditioning system according to the test data acquired by the data acquisition unit; establishing a corresponding relation between the power consumption of the air conditioning system and one or more of the environmental chamber temperature, the air conditioning gear, the set temperature of the air conditioner, the set mode of the air conditioner and the test time according to the power consumption, the test time and the preset test parameters of the air conditioning system; determining the motor efficiency of the motor of the electric vehicle according to the test data acquired by the data acquisition unit; and establishing a corresponding relation among any several of the environment bin temperature, the battery efficiency, the battery capacity, the motor efficiency, the power consumption of the air conditioning system, the test working condition, the energy consumption required by the test working condition, the vehicle quality and the endurance mileage.
The determining the accumulated discharge capacity of the power battery according to the test data collected by the data collecting unit comprises the following steps:
determining the accumulated discharge capacity of the power battery according to the following formula:
Figure 173870DEST_PATH_IMAGE001
wherein, the first and the second end of the pipe are connected with each other,E out_bat represents the accumulated discharge capacity of the power battery, and has a unit of kWh,U bat is the voltage of the power battery, with the unit of V,I bat is the current of the power battery, with the unit of A,Tthe total time of the endurance mileage test is in units of s,ta time variable is represented by a time-series,dta micro variable representing a variable t, which is a limit of an increment of an integral variable;
determining the battery efficiency of the power battery according to the accumulated discharge amount and the charge amount, wherein the method comprises the following steps:
determining the cell efficiency according to the following equation:
Figure 933754DEST_PATH_IMAGE002
wherein, the first and the second end of the pipe are connected with each other,
Figure 182332DEST_PATH_IMAGE003
in order for the efficiency of the battery to be high,E chg represents the charging capacity of the power battery,E out_bat and the accumulated discharge capacity of the power battery is represented.
The motor efficiency of the electric vehicle motor is determined according to the test data collected by the data collection unit, and the method comprises the following steps:
determining the motor efficiency based on the following equation:
Figure 171017DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 719810DEST_PATH_IMAGE005
the efficiency of the motor is shown as such,
Figure 886480DEST_PATH_IMAGE006
which represents the total output energy of the motor,
Figure 622355DEST_PATH_IMAGE007
representing the total input energy of the motor;
Figure 414731DEST_PATH_IMAGE008
Figure 552451DEST_PATH_IMAGE009
Figure 388558DEST_PATH_IMAGE010
wherein, the first and the second end of the pipe are connected with each other,U mot which is indicative of the voltage of the motor,I mot which is representative of the current of the motor,T mot in order to be the torque of the motor,Tfor the total time of the endurance mileage test,tis a variable of the time, and is,nis the rotating speed of the motor, and the rotating speed of the motor,E drv_out representing the output energy of the motor in the driving operation mode,E drv_in representing the input energy of the motor in the drive mode of operation,E brk_out represents the output energy of the motor in the braking operation mode,E brk_in representing the input energy of the motor in the braking mode of operation.
Optionally, because the battery efficiencies at different temperatures are different, a table of the correspondence between the ambient temperature (specifically, the ambient bin temperature in the test system) and the battery efficiency may be established according to the test data, so as to be used for subsequent mileage prediction.
Because the ambient temperature, the air conditioner gear, the air conditioner temperature setting and the air conditioner mode setting are all related to the air conditioner power, and in addition, the power consumption of the air conditioner system is related to the air conditioner power and the test time, a utilization relation table of the power consumption of the air conditioner system, the ambient temperature, the air conditioner gear, the air conditioner temperature setting, the air conditioner mode setting and the test time is constructed and used for predicting the cruising mileage.
Because the motor has two working modes of driving and braking, the motor efficiency is calculated according to two conditions. And the relationship between the motor efficiency and the environment temperature is larger, so that a correlation table of the environment temperature and the motor efficiency is established for predicting the endurance mileage. The energy required by the cycle working condition is also an important factor influencing the endurance mileage, the energy required by the cycle working condition is divided into three parts, the first part is the energy required by the acceleration process and needs to overcome the driving resistance and the acceleration resistance, the second part is the energy required by the uniform speed process, only the driving resistance needs to be overcome at the moment, the third part is the energy required by the braking process, and the energy is not required in the process.
In particular, the energy required for the cycleE cyc_req The formula is as follows:
Figure 611728DEST_PATH_IMAGE011
Figure 942216DEST_PATH_IMAGE012
wherein whena i When the ratio is less than 0, the reaction solution is,E i and =0. In the formula (I), the compound is shown in the specification,E i in order to test the energy of the working condition segment, the working condition segment is a working condition from zero to zero,irepresenting the serial number of each test condition segment.vThe unit is km/h which is the speed of the vehicle under the working condition,t i is a firstiThe time of each test condition segment is in units of s, M is the vehicle mass in units of kg, a is the acceleration in units of M/s 2 D is the distance, in m,f 0 、f 1 、f 2 respectively represent the running resistance coefficient of the automobile.
The embodiment has the following technical effects: a test system for high and low temperature environments is designed, and data such as environmental temperature and vehicle parts can be comprehensively collected. And a endurance mileage test method is designed, the endurance mileage of the electric vehicle under different temperature environments and different working conditions can be accurately tested, and a sample data basis is provided for endurance mileage prediction.
The invention provides a driving range prediction method, which has the advantages that the driving range influence factors of an electric vehicle are more, the influence factors are influenced by factors such as environmental factors, driving condition factors, vehicle factors and the like in a crossed manner, a strong nonlinear relation is realized, and the relation between the driving range of the electric vehicle and the factors is difficult to accurately express by applying a mathematical formula.
On the basis of the above embodiment, an embodiment of the present invention further provides a method for predicting the driving mileage of an electric vehicle, which is executed based on the data obtained by the method for testing the driving mileage of an electric vehicle, and as shown in fig. 4, the method for predicting the driving mileage of an electric vehicle includes the following steps:
and step 410, determining a first characteristic factor of which the relevance with the endurance mileage is greater than a first set threshold value in the data obtained based on the electric vehicle endurance mileage testing method.
The data obtained based on the electric vehicle endurance mileage testing method comprises the following factors and the corresponding relation among any several factors: the system comprises an environmental chamber temperature, a battery efficiency, a battery capacity, a motor efficiency, an air conditioner energy consumption, a test working condition, an energy consumption required by the test working condition, a vehicle quality and a driving mileage.
Optionally, a correlation coefficient method or a gray correlation method is used to determine a first characteristic factor, of which the correlation with the driving range is greater than a first set threshold, in the data obtained by the electric vehicle driving range testing method.
For example, taking the determination of the correlation by using a gray correlation method as an example, the determining of the first characteristic factor, which is greater than a first set threshold in the correlation with the range, in the data obtained by the electric vehicle range testing method includes:
constructing an initial matrix according to the data obtained based on the electric vehicle endurance mileage testing method; carrying out dimensionless processing on the initial matrix to obtain a first matrix; calculating a difference matrix based on the first matrix; determining a maximum and a minimum of the difference matrix; determining a correlation coefficient according to the maximum value and the minimum value; determining grey correlation degree according to the correlation coefficient; and selecting the factor with the gray correlation degree larger than a first set threshold value as the first characteristic factor.
Specifically, an initial matrix is first constructed:
Figure 934442DEST_PATH_IMAGE013
wherein X is an initial matrix, X 1 、X 2 ……X n-1 Respectively representing various factors in the data obtained based on the electric vehicle endurance mileage test method, such as environment cabin temperature, battery capacity, battery efficiency, motor efficiency, air conditioner energy consumption and energy required by test working conditionsConsumption, vehicle mass, etc. factor matrix, X n Representing driving range, x n-1 (m) the m-th test result representing the n-1-th factor, with X 1 As an example, x 1 （1）……x 1 (m) represents a first test result of the first factor, a second test result of the first factor, \8230; and a test result of the mth of the first factor, respectively. x is a radical of a fluorine atom n （1）……x n (m) respectively represents the first test result of the endurance mileage, the 2 nd test result of the endurance mileage, \8230;, and the m th test result of the endurance mileage.
And then carrying out non-dimensionalization processing on the initial matrix to obtain a first matrix Y. In order to avoid negative temperature, the temperature unit adopts thermodynamic temperature scale K.
Figure 708495DEST_PATH_IMAGE014
In the formula
Figure 418962DEST_PATH_IMAGE015
Is any one of the elements of the matrix Y,
Figure 287560DEST_PATH_IMAGE016
i =1,2,3 \ 8230; \8230; n, j =1,2,3 \ 8230; m, any element of the initial matrix X.
The difference matrix δ Y is then calculated:
Figure 399873DEST_PATH_IMAGE017
in the formula
Figure 577782DEST_PATH_IMAGE018
I =1,2,3, 8230, n, j =1,2,3, 8230, m. When calculating the relationship between each factor and the driving range, only i = n may be calculated.
Calculating a maximum value Max and a minimum value Min of the difference matrix:
Figure 775545DEST_PATH_IMAGE019
Figure 447835DEST_PATH_IMAGE020
calculating a correlation coefficient:
Figure 414654DEST_PATH_IMAGE021
in the formula
Figure 920722DEST_PATH_IMAGE022
The correlation coefficient between the ith factor and the nth factor (in the calculation, the nth factor represents the endurance mileage) in the jth test result is shown, wherein rho is a resolution coefficient, rho epsilon (0, 1) generally takes a value of 0.5, i =1,2,3 \8230; 8230; n-1, j =1,2,3 \8230; 8230; m.
Calculating the grey correlation degree:
Figure 215568DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 894811DEST_PATH_IMAGE024
the grey correlation degree between the ith factor and the nth factor is shown, i =1,2,3 \8230, 8230, n-1, j =1,2,3 \8230, and \8230, m, namely, the correlation degree between each factor and the nth factor (in the calculation, the nth factor represents the endurance mileage) can be obtained.
And selecting a factor with the gray correlation degree larger than V as a first characteristic factor, wherein the value of V is generally 0.8. If m1 factors are larger than V, the dimension of the training matrix formed by the first characteristic factors is m1. Therefore, factors with low relevance to the driving mileage can be eliminated, and the calculation efficiency and precision are improved.
And step 420, determining a second characteristic factor with the relevance to the test working condition larger than a second set threshold value from the first characteristic factors.
Optionally, a correlation coefficient method or a gray correlation method is adopted to determine a second characteristic factor, of which the correlation with the test condition is greater than a second set threshold, from the first characteristic factors. Similarly, considering the test condition as the range as described above, the second characteristic factor having a correlation with the test condition greater than the second set threshold may be determined from the first characteristic factors in the manner of calculation in step 410 as described above.
And 430, training the neural network model by taking the second characteristic factors as training samples to obtain a neural network prediction model.
The factors with high relevance with the endurance mileage and the test condition are screened through the step 410 and the step 420 to form a training sample to train the neural network model, and compared with a conventional training method, the accuracy of model prediction can be greatly improved.
And 440, inputting relevant factors of the testing working condition to be predicted into the neural network prediction model to obtain the endurance mileage and relevant control factors.
The relevant factors of the test working condition to be predicted comprise one or more of ambient temperature, battery capacity, battery efficiency, motor efficiency, air conditioner power, vehicle mass and vehicle running resistance; the relevant control factors include one or more of battery energy loss, motor energy loss, and air conditioner energy consumption.
Optionally, the neural network prediction model may be a BP neural network model, the number of hidden layers is 1 or 2, the number of nodes of each hidden layer is 5 or 6, and an excitation function from the input layer to the hidden layer is a tangent S-shaped TansIg function; the excitation function from the hidden layer to the output layer is a PurelIn function; the number of training times was 100, and the learning rate was 0.01.
The endurance mileage prediction algorithm based on the test data provided by the embodiment can accurately predict the endurance mileage of the vehicle.
Further, referring to a schematic flow chart of a method for testing the driving mileage of the electric vehicle shown in fig. 5, the method specifically includes:
the method comprises the steps of screening test data to obtain a training sample, and training a neural network model by using the training sample, wherein the test data specifically comprises test temperature, battery capacity, battery efficiency, motor efficiency, air conditioner power, circulation working conditions and test quality. Specifically, screening various factors in the test data according to the method in the step (410) and the step (420) to obtain a training sample, and training the neural network model based on the training sample to obtain the neural network prediction model. And inputting the environmental temperature, the battery capacity, the battery efficiency, the motor efficiency, the air-conditioning efficiency, the test quality, the resistance coefficient and the circulating condition data of the vehicle to be predicted into a neural network prediction model to obtain the endurance mileage, the battery energy loss, the motor energy loss and the air-conditioning energy consumption.
In summary, after the training of the BP neural network model is completed, relevant parameters of the predicted working condition and vehicle parameters can be input according to the model, and the endurance mileage and the energy loss of relevant parts can be obtained. The high/low temperature endurance mileage test of the pure electric vehicle needs a large amount of time and test resources, and the endurance mileage of the vehicle at each temperature can be predicted according to the method, so that the efficiency is improved, and the cost is reduced.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 6, the electronic device 400 includes one or more processors 401 and memory 402.
The processor 401 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities and may control other components in the electronic device 400 to perform desired functions.
Memory 402 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by processor 401 to implement the electric vehicle range testing system of any of the embodiments of the invention described above and/or other desired functionality. Various contents such as initial external parameters, threshold values, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 403 may include, for example, a keyboard, a mouse, and the like. The output device 404 can output various information to the outside, including warning prompt information, braking force, etc. The output devices 404 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 400 relevant to the present invention are shown in fig. 6, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 400 may include any other suitable components depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the electric vehicle range testing method provided by any of the embodiments of the present invention.
The computer program product may write program code for carrying out operations for embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, an embodiment of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the method for measuring a driving range of an electric vehicle provided by any embodiment of the present invention.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present application. As used in the specification and claims of this application, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, or apparatus comprising the element.
It is further noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," "coupled," and the like are to be construed broadly and encompass, for example, both fixed and removable coupling or integral coupling; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the technical solutions of the embodiments of the present invention.

Claims (7)

1. An electric vehicle driving range prediction method is characterized in that the prediction method is executed based on data obtained by an electric vehicle driving range test method, and comprises the following steps:
determining a first characteristic factor of which the relevance with the endurance mileage is greater than a first set threshold in the data obtained based on the electric vehicle endurance mileage testing method;
determining a second characteristic factor with the relevance to the test working condition larger than a second set threshold value from the first characteristic factors;
training a neural network model by taking the second characteristic factors as training samples to obtain a neural network prediction model;
inputting relevant factors of the test working condition to be predicted into the neural network prediction model to obtain the endurance mileage and relevant control factors;
the relevant factors of the test working condition to be predicted comprise one or more of ambient temperature, battery capacity, battery efficiency, motor efficiency, air conditioner power, vehicle quality and vehicle running resistance; the related control factors comprise one or more of battery energy loss, motor energy loss and air conditioner energy consumption;
the data obtained based on the electric vehicle driving range testing method comprises the following factors and the corresponding relation among any several factors: the system comprises an environment cabin temperature, battery efficiency, battery capacity, motor efficiency, air conditioner energy consumption, test working conditions, energy consumption required by the test working conditions, vehicle quality and endurance mileage;
the determining of the first characteristic factor of which the relevance with the driving range is greater than a first set threshold value in the data obtained based on the electric vehicle driving range testing method comprises the following steps:
constructing an initial matrix according to the data obtained based on the electric vehicle endurance mileage testing method;
carrying out dimensionless processing on the initial matrix to obtain a first matrix;
calculating a difference matrix based on the first matrix;
determining maxima and minima of the difference matrix;
determining the correlation coefficient of each factor and the endurance mileage according to the maximum value and the minimum value;
determining the grey correlation degree of each factor and the endurance mileage according to the correlation coefficient;
selecting a factor with the gray correlation degree larger than a first set threshold value as the first characteristic factor;
the number of hidden layer layers of the neural network prediction model is 1 or 2, the number of nodes of each hidden layer is 5 or 6, and an excitation function from an input layer to the hidden layers is a tangent S-shaped Tansig function;
the excitation function from the hidden layer to the output layer is a PurelIn function;
the number of training times was 100, and the learning rate was 0.01.
2. An electric vehicle driving mileage testing method applied to an electric vehicle driving mileage testing system and an electric vehicle driving mileage predicting method according to claim 1, the testing method comprising:
according to preset test parameters, a bearing frame in the electric vehicle endurance mileage test system is used for setting matched running resistance coefficients and test working conditions, and an air conditioning system of the electric vehicle and the temperature of an environment cabin for simulating the environment temperature are correspondingly set;
when the set conditions are met, starting the test system to carry out the endurance mileage test of the electric vehicle;
in the process of testing the endurance mileage of the electric vehicle, an upper computer in the electric vehicle endurance mileage testing system acquires test data through a data acquisition unit;
when the error between the real-time speed curve displayed by a display screen in the electric vehicle endurance mileage testing system and the curve of the testing working condition reaches an error threshold value, stopping the electric vehicle endurance mileage test, and when the electric vehicle endurance mileage test is stopped, determining the electric quantity consumption of a power battery of the electric vehicle in the test based on the collected test data through the upper computer and recording the driving mileage of the test through the bearing frame;
the preset test parameters comprise one or more of environment bin temperature, test working conditions, the quality of the electric vehicle and relevant parameters of an air conditioner of the electric vehicle.
3. The test method of claim 2, wherein the test data collected by the data collection unit comprises at least: the voltage and the current of a power battery of the electric vehicle, the voltage and the current of an air conditioning system of the electric vehicle and the voltage and the current of a motor of the electric vehicle;
the test method further comprises the following steps:
determining the accumulated discharge capacity of the power battery according to the test data acquired by the data acquisition unit;
determining the battery efficiency of the power battery according to the accumulated discharge amount and the charge amount;
determining the power consumption of the air conditioning system according to the test data acquired by the data acquisition unit;
establishing a corresponding relation between the power consumption of the air conditioning system and one or more of the environmental chamber temperature, the air conditioning gear, the set temperature of the air conditioner, the set mode of the air conditioner and the test time according to the power consumption, the test time and the preset test parameters of the air conditioning system;
determining the motor efficiency of the electric vehicle motor according to the test data acquired by the data acquisition unit;
and establishing a corresponding relation among any several of the environment bin temperature, the battery efficiency, the battery capacity, the motor efficiency, the power consumption of the air conditioning system, the test working condition, the energy consumption required by the test working condition, the vehicle quality and the endurance mileage.
4. The test method according to claim 3, wherein the determining the accumulated discharge capacity of the power battery according to the test data collected by the data collection unit comprises:
determining the accumulated discharge capacity of the power battery according to the following formula:
Figure 353552DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 886164DEST_PATH_IMAGE002
represents the accumulated discharge capacity of the power battery,U bat is the voltage of the power battery and is,I bat is the current of the power battery and is,Tthe total time for the endurance mileage test, t represents a time variable,dta micro variable representing a variable t, which is a limit of an increment of an integral variable;
determining the battery efficiency of the power battery according to the accumulated discharge amount and the charge amount, wherein the method comprises the following steps:
determining the cell efficiency according to the following equation:
Figure 491589DEST_PATH_IMAGE003
wherein, the first and the second end of the pipe are connected with each other,
Figure 691626DEST_PATH_IMAGE004
in order for the efficiency of the cell to be high,E chg and the charging capacity of the power battery is represented.
5. The test method according to claim 3, wherein the determining the motor efficiency of the electric vehicle motor according to the test data collected by the data collection unit comprises:
determining the motor efficiency based on the following equation:
Figure 842247DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 127735DEST_PATH_IMAGE006
the efficiency of the motor is shown as such,
Figure 68009DEST_PATH_IMAGE007
which represents the total output energy of the motor,
Figure 247187DEST_PATH_IMAGE008
representing the total input energy of the motor;
Figure 411452DEST_PATH_IMAGE009
Figure 590760DEST_PATH_IMAGE010
Figure 397042DEST_PATH_IMAGE011
wherein the content of the first and second substances,U mot which is indicative of the voltage of the motor,I mot which is indicative of the current of the motor,T mot in order to be the torque of the motor,Tfor the total time of the endurance mileage test,tis a variable of the time, and is,nthe number of revolutions of the motor is,E drv_out representing the output energy of the motor in the driving operation mode,E drv_in representing the input energy of the motor in the drive mode of operation,E brk_out represents the output energy of the motor in the braking operation mode,E brk_in representing the input energy of the motor in the braking mode of operation.
6. An electronic device, characterized in that the electronic device comprises:
a processor and a memory;
the processor is adapted to perform the method steps of any of claims 1 to 5 by calling a program or instructions stored by the memory.
7. A computer-readable storage medium, characterized in that it stores a program or instructions that cause a computer to perform the method steps of any of claims 1 to 5.
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